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Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets

Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package “eIF4F.analysis”. We describe analysis of copy number status, gene abundance and stoichiometry, survival...

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Detalles Bibliográficos
Autores principales: Wu, Su, Wagner, Gerhard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768376/
https://www.ncbi.nlm.nih.gov/pubmed/36595939
http://dx.doi.org/10.1016/j.xpro.2022.101880
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author Wu, Su
Wagner, Gerhard
author_facet Wu, Su
Wagner, Gerhard
author_sort Wu, Su
collection PubMed
description Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package “eIF4F.analysis”. We describe analysis of copy number status, gene abundance and stoichiometry, survival probability, expression covariation, correlating genes, mRNA/protein correlation, and protein co-expression. Using publicly available large multi-omics data, eIF4F.analysis permits computationally derived and statistically powerful inferences regarding initiation factor regulation in human cancers and clinical relevance of protein interactions within the eIF4F complex. For complete details on the use and execution of this protocol, please refer to Wu and Wagner (2021).(1)
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spelling pubmed-97683762022-12-22 Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets Wu, Su Wagner, Gerhard STAR Protoc Protocol Understanding dysregulation of the eukaryotic initiation factor 4F (eIF4F) complex across tumor types is critical to cancer treatment development. We present a protocol and accompanying R package “eIF4F.analysis”. We describe analysis of copy number status, gene abundance and stoichiometry, survival probability, expression covariation, correlating genes, mRNA/protein correlation, and protein co-expression. Using publicly available large multi-omics data, eIF4F.analysis permits computationally derived and statistically powerful inferences regarding initiation factor regulation in human cancers and clinical relevance of protein interactions within the eIF4F complex. For complete details on the use and execution of this protocol, please refer to Wu and Wagner (2021).(1) Elsevier 2022-12-12 /pmc/articles/PMC9768376/ /pubmed/36595939 http://dx.doi.org/10.1016/j.xpro.2022.101880 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Protocol
Wu, Su
Wagner, Gerhard
Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets
title Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets
title_full Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets
title_fullStr Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets
title_full_unstemmed Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets
title_short Protocol to analyze dysregulation of the eIF4F complex in human cancers using R software and large public datasets
title_sort protocol to analyze dysregulation of the eif4f complex in human cancers using r software and large public datasets
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768376/
https://www.ncbi.nlm.nih.gov/pubmed/36595939
http://dx.doi.org/10.1016/j.xpro.2022.101880
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